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Functional magnetic resonance imaging analytical workflows are highlyflexible with no definite consensus on how to choose a pipeline. While methodshave been developed to explore this analytical space, there is still a lack ofunderstanding of the relationships between the different pipelines. We usecommunity detection algorithms to explore the pipeline space and assess itsstability across different contexts. We show that there are subsets ofpipelines that give similar results, especially those sharing specificparameters (e.g. number of motion regressors, software packages, etc.), withrelative stability across groups of participants. By visualizing thedifferences between these subsets, we describe the effect of pipelineparameters and derive general relationships in the analytical space.

Uncovering communities of pipelines in the task-fMRI analytical space